AI automation: process, approach, and what we need from you

Most business use cases fall into the low-risk category and primarily trigger transparency obligations. Where customers interact directly with the system or AI-generated content is published, we label it. For applications in HR, we look more closely — stricter rules apply there.

That depends on the use case, and we measure it before anything goes live. For classifying incoming messages, well-built systems regularly exceed ninety percent. What matters isn't the rate alone, but what happens when there's an error — that's why we always build in a confidence threshold.

A chatbot answers questions in dialogue. AI automation runs in the background without conversation — it reads, categorizes and pre-decides while someone else is focused on the actual work.

That's the only sensible approach. One use case, eight weeks, measurable outcome. What comes next, you decide based on what you've seen.

No. We exclusively use access where training with customer data is contractually excluded, and we process in the EU. For particularly sensitive data, we use models that run on your own infrastructure.

Every decision is logged: which input, which result, which confidence level. You can spot-check this anytime, just like with a human deputy.

Regular automation follows fixed rules: if field A is filled, do B. AI automation comes into play where there are no fixed fields — free text, images, changing document formats. The right answer is usually both together, not one or the other.